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pro vyhledávání: '"Wen, Lu"'
Semi-supervised learning (SSL) has shown notable potential in relieving the heavy demand of dense prediction tasks on large-scale well-annotated datasets, especially for the challenging multi-organ segmentation (MoS). However, the prevailing class-im
Externí odkaz:
http://arxiv.org/abs/2408.14047
Radiation therapy is the mainstay treatment for cervical cancer, and its ultimate goal is to ensure the planning target volume (PTV) reaches the prescribed dose while reducing dose deposition of organs-at-risk (OARs) as much as possible. To achieve t
Externí odkaz:
http://arxiv.org/abs/2408.13981
Facial Expression Recognition (FER) holds significant importance in human-computer interactions. Existing cross-domain FER methods often transfer knowledge solely from a single labeled source domain to an unlabeled target domain, neglecting the compr
Externí odkaz:
http://arxiv.org/abs/2407.05688
Universal Multi-source Domain Adaptation (UniMDA) transfers knowledge from multiple labeled source domains to an unlabeled target domain under domain shifts (different data distribution) and class shifts (unknown target classes). Existing solutions f
Externí odkaz:
http://arxiv.org/abs/2404.14696
Semi-supervised learning is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation . However, most existing SSL methods predict pixels in a single image independently, ignoring
Externí odkaz:
http://arxiv.org/abs/2403.03512
Radiotherapy is a primary treatment for cancers with the aim of applying sufficient radiation dose to the planning target volume (PTV) while minimizing dose hazards to the organs at risk (OARs). Convolutional neural networks (CNNs) have automated the
Externí odkaz:
http://arxiv.org/abs/2402.04566
To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) images from the corresponding low-dose PET (LPET) images. However, these
Externí odkaz:
http://arxiv.org/abs/2402.00376
Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks. However, most state-of-the-art algorithms require the meta-training tasks to have a dense c
Externí odkaz:
http://arxiv.org/abs/2311.06673
Autor:
Feng, Zhenghao, Wen, Lu, Xiao, Jianghong, Xu, Yuanyuan, Wu, Xi, Zhou, Jiliu, Peng, Xingchen, Wang, Yan
Deep learning (DL) has successfully automated dose distribution prediction in radiotherapy planning, enhancing both efficiency and quality. However, existing methods suffer from the over-smoothing problem for their commonly used L1 or L2 loss with po
Externí odkaz:
http://arxiv.org/abs/2311.02991
Publikováno v:
Shuiwen dizhi gongcheng dizhi, Vol 51, Iss 5, Pp 22-34 (2024)
When simulating drying-rewetting process of grid cells in numerical groundwater modeling using the block-centered finite-difference approach, the models is highly probable to run into non-convergence, which could greatly affect the applicability of g
Externí odkaz:
https://doaj.org/article/93f732c00c1f4799a6254226a9d49587